Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotency

利用遗传变异剖析重编程为多能性的基因调控网络

• 批准号: 10659175
• 负责人: Chongyuan Luo
• 金额: $ 135.13万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659175
• 关键词: ATAC-seq; Affect; Alleles; Architecture; Binding; Biological; Biological Assay; CRISPR interference; Candidate Disease Gene; Cell Differentiation process; Cell Line; Cell Nucleus; Cell Reprogramming; Cells; Cellular Morphology; ChIP-seq; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Computer Models; Consensus; Cytosine; DNA Methylation; Data; Data Set; Disease model; Drug Screening; Enhancers; Event; Fibroblasts; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Engineering; Genetic Transcription; Genetic Variation; Genome; Genomics; Goals; Heterogeneity; Human; Human Genetics; Human Genome; In Situ; Individual; Joints; Knowledge; Maintenance; Maps; Measurement; Mediating; Methods; Methylation; Modeling; Molecular; Molecular Conformation; Mouse Strains; Multiomic Data; Nature; Patients; Phenotype; Population; Population Genetics; Process; Quantitative Trait Loci; RNA; RNA methylation; Regenerative Medicine; Regulation; Regulator Genes; Regulatory Element; Repression; Resolution; Resources; Role; Series; Site; Somatic Cell; Specificity; Statistical Methods; System; Technology; Testing; Therapeutic; Time; Untranslated RNA; Variant; Work; c-myc Genes; causal variant; cell type; embryonic stem cell; epigenome; epigenomics; experimental study; gene interaction; gene regulatory network; genetic variant; genome sequencing; genomic data; histone modification; induced pluripotent stem cell; innovation; insight; molecular phenotype; multiple omics; novel; pluripotency; predictive modeling; programs; repaired; single-cell RNA sequencing; stem cells; transcription factor; transcriptional reprogramming; transcriptome; transcriptomic profiling; transcriptomics; whole genome

PROJECT SUMMARY The reprogramming of somatic human cells to induced pluripotent stem cells (iPSCs) by only four transcription factors (TFs) Oct4, Sox2, Klf4, and cMyc (OSKM) is one of the most striking remodelings of gene regulatory networks. The remarkable ability of OSKM to reprogram diverse somatic cell types into iPSCs that are functionally indistinguishable from embryonic stem cells indicates that OSKM leverages a fundamental mechanism for network remodeling that may be generally applicable to all cell fate transitions. Previous studies of reprogramming have identified the crucial role of cooperative TF binding in repressing somatic programs and activating pluripotent ones. However, associating TF binding dynamics and epigenomic remodeling with key bifurcation events during reprogramming is confounded by the highly heterogeneous nature of the reprogramming process and the lack of knowledge regarding how the transition from somatic to pluripotent regulatory programs occurs in individual cells. In this project, we aim to model the regulatory network underlying the cell fate change of reprogramming using three types of single-cell multi-omic profiles generated from critical time points during reprogramming. We will interrogate the network leveraging natural perturbation of reprogramming and pluripotency by genetic variants. Genetic variation is well known to modulate the regulatory network of pluripotency and contributes to the variability of cellular phenotypes and differentiation capacity of iPSC lines. We will generate population-scale single-cell joint profiling of RNA and DNA methylation (snmCT- seq), joint profiling of RNA and chromatin accessibility (scRNA + ATAC-seq) and single-nucleus joint profiling of chromatin conformation and DNA methylation (sn-m3C-seq), allowing the cell-type-specific determination of transcriptome, chromatin accessibility and methylation states at regulatory elements, as well as enhancer-gene looping to connect non-coding variants to their regulatory target. To integrate OSKM binding with the single-cell transcriptomic and epigenomic dynamics, we will determine the allele-specific binding of TFs and histone modifications using a pooled-alleles ChIP-seq strategy. We will use Dynamic Regulatory Events Miner (DREM) to construct predictive models by integrating transcription factor-gene interaction information with time- and pseudotime-series genomics data. To determine the genetic regulation of the reprogramming network, we will apply the novel statistical method FastGxE to distinguish cell-type-specific from the shared genetic component of gene expression regulation, to enhance the sensitivity for identifying cell-type-specific quantitative trait loci (QTLs). To test the regulatory network, we will experimentally determine the function of network hub genes and non-coding variants using high-throughput CRISPR interference and precise variant replacement experiments. Our proposed project integrates diverse approaches including single-cell multi-omics, computational modeling, and genetic engineering, and will likely provide new insights into the mechanism by which TFs remodel regulatory networks of cell type identity and serve as a model for similar analyses in other systems.

项目概要 仅通过四个转录即可将人类体细胞重编程为诱导多能干细胞（iPSC） 因子 (TF) Oct4、Sox2、Klf4 和 cMyc (OSKM) 是基因调控最引人注目的重塑之一 网络。 OSKM 将多种体细胞类型重编程为 iPSC 的卓越能力 与胚胎干细胞在功能上无法区分，这表明 OSKM 利用了一种基本的 网络重塑机制可能普遍适用于所有细胞命运转变。之前的研究 重编程的研究人员已经确定了协同转录因子结合在抑制体细胞程序中的关键作用 激活多能细胞。然而，将 TF 结合动力学和表观基因组重塑与关键 重编程期间的分岔事件因高度异质性而混淆 重编程过程以及缺乏关于如何从体细胞向多能转变的知识 调节程序发生在单个细胞中。在这个项目中，我们的目标是对底层的监管网络进行建模 使用从关键生成的三种类型的单细胞多组学概况进行重编程的细胞命运变化 重新编程期间的时间点。我们将利用自然扰动来询问网络 通过遗传变异进行重编程和多能性。众所周知，遗传变异可以调节调节 多能性网络并有助于细胞表型的变异性和分化能力 iPSC 线。我们将生成群体规模的 RNA 和 DNA 甲基化联合分析 (snmCT- seq）、RNA 和染色质可及性的联合分析（scRNA + ATAC-seq）以及单核联合分析 染色质构象和 DNA 甲基化 (sn-m3C-seq)，允许细胞类型特异性测定 转录组、染色质可及性和调节元件的甲基化状态以及增强子基因 循环将非编码变体与其监管目标连接起来。将 OSKM 结合与单细胞集成 转录组和表观基因组动力学，我们将确定 TF 和组蛋白的等位基因特异性结合 使用混合等位基因 ChIP-seq 策略进行修改。我们将使用动态监管事件矿工（DREM） 通过将转录因子-基因相互作用信息与时间和时间相结合来构建预测模型 伪时间序列基因组数据。为了确定重编程网络的遗传调控，我们将 应用新颖的统计方法 FastGxE 来区分细胞类型特异性和共享遗传成分 基因表达调控，提高识别细胞类型特异性数量性状位点的灵敏度 （QTL）。为了测试调控网络，我们将通过实验确定网络枢纽基因的功能和 使用高通量 CRISPR 干扰和精确的变体替换实验来检测非编码变体。 我们提出的项目整合了多种方法，包括单细胞多组学、计算建模、 和基因工程，并可能为 TF 重塑监管机制提供新的见解 细胞类型识别网络，并作为其他系统中类似分析的模型。